Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

Aldabbas, Hamza; Bajahzar, Abdullah; Alruily, Meshrif; Qureshi, Ali Adil; Latif, Rana M. Amir; Farhan, Muhammad

doi:10.1515/jisys-2019-0197

Cited by 21 publications

(7 citation statements)

References 24 publications

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“…Thus, the presence of one does not affect the other. That is why they are called naive [69]. Logistic regression, a statistical ML algorithm [69] that fits a regression surface to data in the case of dichotomous dependent variables [70], was applied in four different projects, and the highest accuracy was 96%, as shown in Table 5.…”

Section: Yearmentioning

confidence: 99%

“…That is why they are called naive [69]. Logistic regression, a statistical ML algorithm [69] that fits a regression surface to data in the case of dichotomous dependent variables [70], was applied in four different projects, and the highest accuracy was 96%, as shown in Table 5. Sobnath et al [48] applied Applying Data Mining in Graduates' Employability: A Systematic Literature Review this algorithm to a dataset of 270,934 records of students with a known disability in UK schools since logistic regression requires large sample sizes to achieve an adequate level of stability [71].…”

Section: Yearmentioning

confidence: 99%

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Applying Data Mining in Graduates’ Employability

2023

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Envisaging an adequate IT/IS solution that can mitigate the employability problems is imperative because nowadays there is a high rate of unemployed graduates. Thus, the main goal of this systematic literature review (SLR) was to explore the application of data mining techniques in modeling employability and see how those techniques have been applied and which factors/variables have been retained to be the most predictors or/and prescribers of employability. Data mining techniques have shown the ability to serve as decision support tools in predicting and even prescribing employability. The review determined and analyzed the machine learning algorithms used in data mining to either predict or prescribe employability. This review used the PRISMA method to determine which studies from the existing literature to include as items for this SLR. Hence, 20 relevant studies, 16 of which are predicting employability and 4 of which are prescribing employability. These studies were selected from reliable databases: ScienceDirect, Springer, Wiley, IEEE Xplore, and Taylor and Francis. According to the results of this study, various data mining techniques can be used to predict and/or to prescribe employability. Furthermore, the variables/factors that predict and prescribe employability vary by country and the type of prediction or prescription conducted research. Nevertheless, all previous studies have relied more on skill as the main factor that predict and/or prescribe employability in developed countries and none studies have been conducted in unstable developing countries. Therefore, the need to conduct research on predicting or prescribing employability in such countries by trying to use contextual factors beyond skill as features.

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Section: Yearmentioning

confidence: 99%

Section: Yearmentioning

confidence: 99%

Applying Data Mining in Graduates’ Employability

2023

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“…• Aldabbas et al [163] presented an approach to classify the user reviews collected by scrapping from the Google Play platform into 3 categories: positive, negative, and neutral. The semantic analysis of these reviews was done using NLP and ML classifiers such as LR, RF, and Multinomial Naïve Bayes (MNB).…”

Section: Social Mediamentioning

confidence: 99%

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Tyagi

Bhushan

2023

Wireless Pers Commun

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Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities.

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“…For example, the word "Fraud" can be divided into a bigram of "Fr", "ra", "au", and "ud". The bigram frequency distribution analysis can be used in many fields of application, including natural language processing and cryptography [37,38]. The proposed method uses the bigram frequency distribution of social media messages to create a novel mapping concept based on the bigram character and ZWC combination to represent the watermark.…”

Section: F Character Bigrammentioning

confidence: 99%

Improving ANiTW Performance Using Bigrams Character Encoding and Identity-Based Signature

Rofiatunnajah

Barmawi

2023

IEEE Access

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Several threats, such as misinformation and hoax, exist in social media as a frequently used technology to exchange information. These threats can be prevented if the reader can verify the message's content integrity and source authenticity. One recent solution to overcome these threats was proposed by Ahvanooey et al. called ANiTW. ANiTW uses an invisible text watermarking scheme to verify social media message integrity and identify the message source. However, ANiTW is still vulnerable to undetected malicious text modification. Moreover, ANiTW has no tools to authenticate the extracted message source. The proposed method aims to overcome those vulnerabilities and improve ANiTW performance as a tool to verify message content integrity and message source authenticity in social media. To overcome vulnerabilities in ANITW, a part of the message hash and IBS-based signature is introduced as the watermark. The message integrity is verifiable by using part of the message hash as a watermark to detect any malicious. Moreover, implementing an IBS-based signature enables any reader to verify the message source's authenticity easily. In addition, to improve ANITW performance in embedding capacity and invisibility, a novel bigram-based character encoding and embedding rule are also introduced. Based on the experiment and analysis result, it is proven that the proposed method can perform better than ANiTW in the security, embedding capacity, and invisibility aspects.

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Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

Cited by 21 publications

References 24 publications

Applying Data Mining in Graduates’ Employability

Applying Data Mining in Graduates’ Employability

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Improving ANiTW Performance Using Bigrams Character Encoding and Identity-Based Signature

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